• DocumentCode
    1405858
  • Title

    Variational Gaussian process classifiers

  • Author

    Gibbs, Mark N. ; MacKay, David J C

  • Author_Institution
    Cavendish Lab., Cambridge Univ., UK
  • Volume
    11
  • Issue
    6
  • fYear
    2000
  • fDate
    11/1/2000 12:00:00 AM
  • Firstpage
    1458
  • Lastpage
    1464
  • Abstract
    Gaussian processes are a promising nonlinear regression tool, but it is not straightforward to solve classification problems with them. In the paper the variational methods of Jaakkola and Jordan (2000) are applied to Gaussian processes to produce an efficient Bayesian binary classifier.
  • Keywords
    Bayes methods; Gaussian processes; covariance matrices; neural nets; pattern classification; Bayesian binary classifier; nonlinear regression tool; variational Gaussian process classifiers; variational methods; Bayesian methods; Covariance matrix; Gaussian approximation; Gaussian distribution; Gaussian processes; Monte Carlo methods; Neural networks; Parametric statistics; Predictive models; Probability distribution;
  • fLanguage
    English
  • Journal_Title
    Neural Networks, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1045-9227
  • Type

    jour

  • DOI
    10.1109/72.883477
  • Filename
    883477
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1405858